octave: Profiler Example
13.7 Profiler Example
=====================
Below, we will give a short example of a profiler session. 
Profiling, for the documentation of the profiler functions in detail.
Consider the code:
global N A;
N = 300;
A = rand (N, N);
function xt = timesteps (steps, x0, expM)
global N;
if (steps == 0)
xt = NA (N, 0);
else
xt = NA (N, steps);
x1 = expM * x0;
xt(:, 1) = x1;
xt(:, 2 : end) = timesteps (steps - 1, x1, expM);
endif
endfunction
function foo ()
global N A;
initial = @(x) sin (x);
x0 = (initial (linspace (0, 2 * pi, N)))';
expA = expm (A);
xt = timesteps (100, x0, expA);
endfunction
function fib = bar (N)
if (N <= 2)
fib = 1;
else
fib = bar (N - 1) + bar (N - 2);
endif
endfunction
If we execute the two main functions, we get:
tic; foo; toc;
⇒ Elapsed time is 2.37338 seconds.
tic; bar (20); toc;
⇒ Elapsed time is 2.04952 seconds.
But this does not give much information about where this time is
spent; for instance, whether the single call to ‘expm’ is more expensive
or the recursive time-stepping itself. To get a more detailed picture,
we can use the profiler.
profile on;
foo;
profile off;
data = profile ("info");
profshow (data, 10);
This prints a table like:
# Function Attr Time (s) Calls
---------------------------------------------
7 expm 1.034 1
3 binary * 0.823 117
41 binary \ 0.188 1
38 binary ^ 0.126 2
43 timesteps R 0.111 101
44 NA 0.029 101
39 binary + 0.024 8
34 norm 0.011 1
40 binary - 0.004 101
33 balance 0.003 1
The entries are the individual functions which have been executed
(only the 10 most important ones), together with some information for
each of them. The entries like ‘binary *’ denote operators, while other
entries are ordinary functions. They include both built-ins like ‘expm’
and our own routines (for instance ‘timesteps’). From this profile, we
can immediately deduce that ‘expm’ uses up the largest proportion of the
processing time, even though it is only called once. The second
expensive operation is the matrix-vector product in the routine
‘timesteps’. (1)
Timing, however, is not the only information available from the
profile. The attribute column shows us that ‘timesteps’ calls itself
recursively. This may not be that remarkable in this example (since
it’s clear anyway), but could be helpful in a more complex setting. As
to the question of why is there a ‘binary \’ in the output, we can
easily shed some light on that too. Note that ‘data’ is a structure
array (Structure Arrays) which contains the field
‘FunctionTable’. This stores the raw data for the profile shown. The
number in the first column of the table gives the index under which the
shown function can be found there. Looking up ‘data.FunctionTable(41)’
gives:
scalar structure containing the fields:
FunctionName = binary \
TotalTime = 0.18765
NumCalls = 1
IsRecursive = 0
Parents = 7
Children = [](1x0)
Here we see the information from the table again, but have additional
fields ‘Parents’ and ‘Children’. Those are both arrays, which contain
the indices of functions which have directly called the function in
question (which is entry 7, ‘expm’, in this case) or been called by it
(no functions). Hence, the backslash operator has been used internally
by ‘expm’.
Now let’s take a look at ‘bar’. For this, we start a fresh profiling
session (‘profile on’ does this; the old data is removed before the
profiler is restarted):
profile on;
bar (20);
profile off;
profshow (profile ("info"));
This gives:
# Function Attr Time (s) Calls
-------------------------------------------------------
1 bar R 2.091 13529
2 binary <= 0.062 13529
3 binary - 0.042 13528
4 binary + 0.023 6764
5 profile 0.000 1
8 false 0.000 1
6 nargin 0.000 1
7 binary != 0.000 1
9 __profiler_enable__ 0.000 1
Unsurprisingly, ‘bar’ is also recursive. It has been called 13,529
times in the course of recursively calculating the Fibonacci number in a
suboptimal way, and most of the time was spent in ‘bar’ itself.
Finally, let’s say we want to profile the execution of both ‘foo’ and
‘bar’ together. Since we already have the run-time data collected for
‘bar’, we can restart the profiler without clearing the existing data
and collect the missing statistics about ‘foo’. This is done by:
profile resume;
foo;
profile off;
profshow (profile ("info"), 10);
As you can see in the table below, now we have both profiles mixed
together.
# Function Attr Time (s) Calls
---------------------------------------------
1 bar R 2.091 13529
16 expm 1.122 1
12 binary * 0.798 117
46 binary \ 0.185 1
45 binary ^ 0.124 2
48 timesteps R 0.115 101
2 binary <= 0.062 13529
3 binary - 0.045 13629
4 binary + 0.041 6772
49 NA 0.036 101
---------- Footnotes ----------
(1) We only know it is the binary multiplication operator, but
fortunately this operator appears only at one place in the code and thus
we know which occurrence takes so much time. If there were multiple
places, we would have to use the hierarchical profile to find out the
exact place which uses up the time which is not covered in this example.
Info Catalog
octave: Profiling
octave: Debugging